Senior Machine Learning Engineer

Senior Machine Learning & Data Platform Engineer

AI, & Real Time Systems

$170,000 - $200,000 base + bonus + equity

Remote - Work from anywhere (US Based Preferred)

We're partnering with a fast-growing, AI-driven company building real-time ad infrastructure and personalized commerce platforms. This isn't a website - it's core systems powering automated, high-performance advertising for major brands and retailers.

As they continue to scale, machine learning is becoming a core part of their growth strategy, and they're looking for a Senior Machine Learning & Data Platform Engineer to build the production-grade ML infrastructure that powers intelligent decision-making across the entire platform.

This isn't a research-focused role. It's an opportunity to build and scale machine learning systems that directly influence millions of advertising and commerce decisions every day.

What you'd be working on:

• Building and scaling the machine learning platform that powers optimisation, personalisation, and decision-making across the business
• Developing production ML models focused on CTR prediction, conversion prediction, recommendation systems, dynamic bidding, and performance optimisation
• Creating scalable feature engineering pipelines and automated MLOps workflows for training, deployment, monitoring, and retraining
• Analysing large-scale commerce and advertising datasets to improve model performance and business outcomes
• Designing and running A/B tests and experiments to validate model effectiveness
• Building low-latency APIs and real-time prediction services at scale
• Partnering with Product, Engineering, and Data teams to embed machine learning into core platform capabilities

What they're looking for:

• 5+ years building production machine learning systems, data platforms, or large-scale analytics infrastructure
• Strong Python and SQL skills
• Experience deploying ML models into production environments
• Hands-on experience with TensorFlow, PyTorch, scikit-learn, XGBoost, or similar frameworks
• Experience with feature engineering, large-scale datasets, and distributed technologies such as Spark, Databricks, or Kafka
• Strong understanding of MLOps, experimentation, predictive modelling, and recommendation systems
• Familiarity with cloud-native infrastructure, Kubernetes, containers, and scalable APIs

Nice to have:

• Experience within AdTech, Retail Media, E-commerce, or recommendation engines
• Experience working with metrics such as CTR, CPC, CPA, and ROAS
• Exposure to feature stores, MLflow, vector databases, or real-time inference systems
• Experience with personalisation, ranking models, reinforcement learning, or dynamic pricing

Compensation:

• $170,000 - $200,000 base salary
• Bonus
• Equity/stock options

Interview Process:

• Introductory conversation with the Founder
• Technical interview with Engineering leadership
• Take-home exercise followed by a collaborative discussion focused on your approach, architecture decisions, and problem-solving process